Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems
This work addresses the need for efficient and accurate semantic retrieval in question answering systems, representing an incremental improvement.
The authors tackled the problem of finding the most similar question in QA systems by proposing a Multi-task Sentence Encoding Model (MSEM) for Paraphrase Identification, which outperformed existing sentence matching models in experiments.
Question Answering (QA) systems are used to provide proper responses to users' questions automatically. Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem. Given a question, the aim of the task is to find the most similar question from a QA knowledge base. In this paper, we propose a Multi-task Sentence Encoding Model (MSEM) for the PI problem, wherein a connected graph is employed to depict the relation between sentences, and a multi-task learning model is applied to address both the sentence matching and sentence intent classification problem. In addition, we implement a general semantic retrieval framework that combines our proposed model and the Approximate Nearest Neighbor (ANN) technology, which enables us to find the most similar question from all available candidates very quickly during online serving. The experiments show the superiority of our proposed method as compared with the existing sentence matching models.